Intrinsic Motivation. Curious agents are designed to search for novelty in the environment and to discover new behaviours, driven by an intrinsically motivated signal. Practically, this comes in the form of self-generated rewards \(r^{(i)}\)  that can complement or replace the external rewards \(r^{(e)}\)  of the environment. The combined reward at time step \(t\)  can be represented as: \(r_t = \eta _e r^{(e)}_t + \eta _i r^{(i)}_t\) , where \(\eta _e\)  and \(\eta _i\)  are factors adopted to balance external and intrinsic rewards. How to optimally balance between exploration with intrinsic motivation and exploitation of external rewards is still an unanswered question, which we do not aim to address with our method. Instead, similarly to what done in other works [1]}, [2]}, [3]}, [4]}, [5]}, we focus on the exploration behaviour emerging from the self-supervised intrinsic motivation signal.
